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  • European Marine Science
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  • European Commission
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ina M. Deutschmann; Erwan Delage; Caterina R. Giner; Marta Sebastián; +9 Authors

    Microbial interactions are vital in maintaining ocean ecosystem function, yet their dynamic nature and complexity remain largely unexplored. Here, we use association networks to investigate possible ecological interactions in the marine microbiome among archaea, bacteria, and picoeukaryotes throughout different depths and geographical regions of the tropical and subtropical global ocean. Our findings reveal that potential microbial interactions change with depth and geographical scale, exhibiting highly heterogeneous distributions. A few potential interactions were global, meaning they occurred across regions at the same depth, while 11-36% were regional within specific depths. The bathypelagic zone had the lowest proportion of global associations, and regional associations increased with depth. Moreover, we observed that most surface water associations do not persist in deeper ocean layers despite microbial vertical dispersal. Our work contributes to a deeper understanding of the tropical and subtropical global ocean interactome, which is essential for addressing the challenges posed by global change Sampling was carried out thanks to the Consolider-Ingenio program (project Malaspina 2010 Expedition, ref. CSD2008–00077, to C.M.D.) and HOTMIX project (CTM2011-30010/MAR, to J.A.), funded by the Spanish Ministry of Economy and Competitiveness Science and Innovation. [...] I.M.D., R.L., and R.M. received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 675752 (SINGEK, http://www.singek.eu). R.L. was supported by a Ramón y Cajal fellowship (RYC-2013-12554, MINECO, Spain). This work was also supported by the projects INTERACTOMICS (CTM2015-69936-P, MINECO, Spain), MicroEcoSystems (240904, RCN, Norway), and MINIME (PID2019-105775RB-I00, AEI, Spain) to R.L. S.C. was supported by the CNRS MITI through the interdisciplinary program Modélisation du Vivant (GOBITMAP grant). S.C., D.E., and S.G.A. were funded by the H2020 project AtlantECO (award number 862923). We acknowledge funding of the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation to the ICM-CSIC (CEX2019-000928-S) 17 pages, 6 figures, 2 tables, supplementary information https://doi.org/10.1038/s41467-023-44550-y.-- Data availability: DNA sequence data is publicly available at the European Nucleotide Archive (see details in Table 2). The accession numbers for the different datasets are: MalaSurf (PRJEB23913, PRJEB25224), MalaVP (PRJEB23771, PRJEB45015), MalaDeep (PRJEB45011, PRJEB45014), Hotmix (PRJEB44683, PRJEB44474). OTU tables and source data to generate the figures and tables are provided in GitHub (https://github.com/InaMariaDeutschmann/GlobalNetworkMalaspinaHotmix) and Zenodo: https://doi.org/10.5281/zenodo.1023007337. The following databases have been used: SILVA v13289, PR2 v4.11.190, and the World Ocean Database 201391.-- Code availability: The code for data analysis, including commands to run FlashWeave and EnDED (environmentally-driven-edge-detection and computing Jaccard index), is publicly available at GitHub (https://github.com/InaMariaDeutschmann/GlobalNetworkMalaspinaHotmix) and Zenodo37 Peer reviewed

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    Nature Communications
    Article . 2024 . Peer-reviewed
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    DIGITAL.CSIC
    Article . 2024 . Peer-reviewed
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      Nature Communications
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Udita Chandola; Camille Trottier; Marinna Gaudin; Louis Josselin Lavier Aydat; +10 Authors

    AbstractNon-cyanobacteria diazotrophs (NCDs) were shown to dominate in surface waters shifting the long-held paradigm of cyanobacteria dominance and raising fundamental questions on how these putative heterotrophic bacteria thrive in sunlit oceans. Here, we report an unprecedented finding in the widely used model diatomPhaeodactylum tricornutum(Pt) of NCDs sustaining diatom cells in the absence of bioavailable nitrogen. We identifiedPtNCDs using metagenomics sequencing and detected nitrogenase genesin silicoand/or by PCR. We demonstrated nitrogen fixation withinPtNCDs while establishing their genetic affiliation to NCDs found in the environment. We showed the wide occurrence of this type of interactions with the isolation of NCDs from other microalgae, their identification in the environment, and their predicted associations with photosynthetic microalgae. Our findings will help to understand the different players driving global marine nitrogen fixation. Overall, this study provides evidence for a hitherto unrecognized symbiosis that affects marine element cycling and primary production using a multidisciplinary model-based approach.

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    https://doi.org/10.21203/rs.3....
    Preprint . 2023
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    HAL-Rennes 1
    Preprint . 2022
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    Authors: T. Uchida; J. Le Sommer; C. Stern; R. P. Abernathey; +26 Authors

    Abstract. With the increase in computational power, ocean models with kilometer-scale resolution have emerged over the last decade. These models have been used for quantifying the energetic exchanges between spatial scales, informing the design of eddy parametrizations, and preparing observing networks. The increase in resolution, however, has drastically increased the size of model outputs, making it difficult to transfer and analyze the data. It remains, nonetheless, of primary importance to assess more systematically the realism of these models. Here, we showcase a cloud-based analysis framework proposed by the Pangeo project that aims to tackle such distribution and analysis challenges. We analyze the output of eight submesoscale-permitting simulations, all on the cloud, for a crossover region of the upcoming Surface Water and Ocean Topography (SWOT) altimeter mission near the Gulf Stream separation. The cloud-based analysis framework (i) minimizes the cost of duplicating and storing ghost copies of data and (ii) allows for seamless sharing of analysis results amongst collaborators. We describe the framework and provide example analyses (e.g., sea-surface height variability, submesoscale vertical buoyancy fluxes, and comparison to predictions from the mixed-layer instability parametrization). Basin- to global-scale, submesoscale-permitting models are still at their early stage of development; their cost and carbon footprints are also rather large. It would, therefore, benefit the community to document the different model configurations for future best practices. We also argue that an emphasis on data analysis strategies would be crucial for improving the models themselves.

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    Authors: Tara Ocean Foundation; Abreu, Andre; Bourgois, Etienne; Gristwood, Adam; +45 Authors

    Microbial communities have essential roles in ocean ecology and planetary health. Microbes participate in nutrient cycles, remove huge quantities of carbon dioxide from the air and support ocean food webs. The taxonomic and functional diversity of the global ocean microbiome has been revealed by technological advances in sampling, DNA sequencing and bioinformatics. A better understanding of the ocean microbiome could underpin strategies to address environmental and societal challenges, including achievement of multiple Sustainable Development Goals way beyond SDG 14 ‘life below water’. We propose a set of priorities for understanding and protecting the ocean microbiome, which include delineating interactions between microbiota, sustainably applying resources from oceanic microorganisms and creating policy- and funder-friendly ocean education resources, and discuss how to achieve these ambitious goals We thank R. Zaayman-Gallant, T. Rauscher and F. Ibarbalz for preparation of the figures, and the European Union’s Horizon 2020 research and innovation project AtlantECO, under grant agreement no. 862923 With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S) This article is contribution number 131 of Tara Oceans.-- 11 pages, 5 figures, 1 table, 1 box Peer reviewed

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    Nature Microbiology
    Other literature type . Article . 2022 . Peer-reviewed
    License: Springer TDM
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Churcheward, Benjamin; Millet, Maxime; Bihouée, Audrey; Fertin, Guillaume; +1 Authors

    ABSTRACTMetagenome-Assembled Genomes (MAGs) represent individual genomes recovered from metagenomic data. MAGs are extremely useful to analyse uncultured microbial genomic diversity, as well as to characterize associated functional and metabolic potential in natural environments. Recent computational developments have considerably improved MAGs reconstruction but also emphasized several limitations, such as the non-binning of sequence regions with repetitions or distinct nucleotidic composition. Different assembly and binning strategies are often used, however, it still remains unclear which assembly strategy in combination with which binning approach, offers the best performance for MAGs recovery. Several workflows have been proposed in order to reconstruct MAGs, but users are usually limited to single-metagenome assembly or need to manually define sets of metagenomes to co-assemble prior to genome binning. Here, we present MAGNETO, an automated workflow dedicated to MAGs reconstruction, which includes a fully-automated co-assembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAGs recovery. MAGNETO is implemented as a Snakemake workflow and is available at: https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto.IMPORTANCEGenome-resolved metagenomics has led to the discovery of previously untapped biodiversity within the microbial world. As the development of computational methods for the recovery of genomes from metagenomes continues, existing strategies need to be evaluated and compared to eventually lead to standardized computational workflows. In this study, we compared commonly used assembly and binning strategies and assessed their performance using both simulated and real metagenomic datasets. We propose a novel approach to automate co-assembly, avoiding the requirement for a priori knowledge to combine metagenomic information. The comparison against a previous co-assembly approach demonstrates a strong impact of this step on genome binning results, but also the benefits of informing co-assembly for improving the quality of recovered genomes. MAGNETO integrates complementary assembly-binning strategies to optimize genome reconstruction and provides a complete reads-to-genomes workflow for the growing microbiome research community.

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    mSystems
    Article . 2022 . Peer-reviewed
    License: CC BY
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    Article . 2022
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      mSystems
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    Authors: Roy, Amédée; Fablet, Ronan; Bertrand, Sophie Lanco;

    Abstract Miniature electronic devices have recently enabled ecologists to document relatively large amounts of animal trajectories. Modelling such trajectories may contribute to explaining the mechanisms underlying observed behaviours and to clarifying ecological processes at the scale of the population by simulating multiple trajectories. Existing approaches to animal movement modelling have mainly addressed the first objective, and are often limited when used for simulation purposes. Individual‐based models generally rely on ad hoc formulation and their empirical parametrization lacks generability, while random walks based on mathematically sound statistical inference typically consist of first‐order Markovian models calibrated at the local scale which may lead to overly simplistic description and simulation of animal trajectories. We investigate a recent deep learning tool—generative adversarial networks (GAN)—to simulate animal trajectories. GANs consist of a pair of deep neural networks that aim to capture the data distribution of some experimental dataset. They enable the generation of new instances of data that share statistical properties. This study aims at identifying relevant deep network architectures to simulate central‐place foraging trajectories, as well as at evaluating GANs drawbacks and benefits over classical methods, such as state‐switching hidden Markov models (HMM). We demonstrate the outstanding ability of deep convolutional GANs to simulate and to capture medium‐ to large‐scale properties of seabird foraging trajectories. GAN‐derived synthetic trajectories reproduced the Fourier spectral density of observed trajectories better than those simulated using HMMs. However, unlike HMMs, GANs do not adequately capture local‐scale descriptive statistics, such as step speed distributions. GANs provide a new likelihood‐free approach to calibrate complex stochastic processes and thus open new research avenues for animal movement modelling. We discuss the potential uses of GANs in movement ecology and future developments to better capture local‐scale features. In this context, embedding HMM‐based priors in GAN schemes appears as a promising research direction.

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    Authors: Pauthenet, Etienne; Bachelot, Loïc; Balem, Kevin; Maze, Guillaume; +4 Authors

    Despite the ever-growing number of ocean data, the interior of the ocean remains undersampled in regions of high variability such as the Gulf Stream. In this context, neural networks have been shown to be effective for interpolating properties and understanding ocean processes. We introduce OSnet (Ocean Stratification network), a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The proposed scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T−S) profiles down to 1000 m and the mixed-layer depth (MLD) from surface data covering 1993 to 2019. OSnet is trained to fit sea surface temperature and sea level anomalies onto all historical in situ profiles in the Gulf Stream region. To achieve vertical coherence of the profiles, the MLD prediction is used to adjust a posteriori the vertical gradients of predicted T−S profiles, thus increasing the accuracy of the solution and removing vertical density inversions. The prediction is generalized on a 1/4∘ daily grid, producing four-dimensional fields of temperature and salinity, with their associated confidence interval issued from the bootstrap. OSnet profiles have root mean square error comparable with the observation-based Armor3D weekly product and the physics-based ocean reanalysis Glorys12. The lowest confidence in the prediction is located north of the Gulf Stream, between the shelf and the current, where the thermohaline variability is large. The OSnet reconstructed field is coherent even in the pre-Argo years, demonstrating the good generalization properties of the network. It reproduces the warming trend of surface temperature, the seasonal cycle of surface salinity and mesoscale structures of temperature, salinity and MLD. While OSnet delivers an accurate interpolation of the ocean stratification, it is also a tool to study how the ocean stratification relates to surface data. We can compute the relative importance of each input for each T−S prediction and analyse how the network learns which surface feature influences most which property and at which depth. Our results demonstrate the potential of machine learning methods to improve predictions of ocean interior properties from observations of the ocean surface.

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    Ocean Science (OS)
    Other literature type . 2022
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    https://doi.org/10.5194/egusph...
    Preprint . 2022 . Peer-reviewed
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    Article . 2022
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    Authors: Marta Royo-Llonch; Pablo Sánchez; Clara Ruiz-González; Guillem Salazar; +36 Authors

    The role of the Arctic Ocean ecosystem in climate regulation may depend on the responses of marine microorganisms to environmental change. We applied genome-resolved metagenomics to 41 Arctic seawater samples, collected at various depths in different seasons during the Tara Oceans Polar Circle expedition, to evaluate the ecology, metabolic potential and activity of resident bacteria and archaea. We assembled 530 metagenome-assembled genomes (MAGs) to form the Arctic MAGs catalogue comprising 526 species. A total of 441 MAGs belonged to species that have not previously been reported and 299 genomes showed an exclusively polar distribution. Most Arctic MAGs have large genomes and the potential for fast generation times, both of which may enable adaptation to a copiotrophic lifestyle in nutrient-rich waters. We identified 38 habitat generalists and 111 specialists in the Arctic Ocean. We also found a general prevalence of 14 mixotrophs, while chemolithoautotrophs were mostly present in the mesopelagic layer during spring and autumn. We revealed 62 MAGs classified as key Arctic species, found only in the Arctic Ocean, showing the highest gene expression values and predicted to have habitat-specific traits. The Artic MAGs catalogue will inform our understanding of polar microorganisms that drive global biogeochemical cycles This work acknowledges the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). We thank the commitment of the following sponsors and research funding agencies: the Spanish Ministry of Economy and Competitiveness (project MAGGY, grant no. CTM2017-87736-R and Polar EcoGen PID2020-116489RB-I00), Horizon 2020-Research and Innovation Framework Programme (Atlantic ECOsystems assessment, forecasting & sustainability, grant no. H2020-BG-2019-2), Centre National de la Recherche Scientifique (in particular Groupement de Recherche GDR3280 and the Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans-GOSEE), European Molecular Biology Laboratory, Genoscope/Commissariat à l’Énergie Atomique et aux Énergies Alternatives, the French Ministry of Research and the French Government’s ‘Investissements d’Avenir’ programmes OCEANOMICS (project no. ANR-11-BTBR-0008), FRANCE GENOMIQUE (project no. ANR-10-INBS-09-08), MEMO LIFE (project no. ANR-10-LABX-54), Paris Sciences et Lettres University (project no. ANR-11-IDEX-0001-02), Eidgenössische Technische Hochschule Zürich and Helmut Horten Foundation, the Swiss National Foundation (project no. 205321_184955), MEXT/JSPS/KAKENHI (project nos. 16H06429, 16K21723, 16H06437 and 18H02279) 14 pages, 6 figures, additional information https://doi.org/10.1038/s41564-021-00979-9.-- Data availability: Accession numbers for the data used and generated in this study can be found in Supplementary Table 12, which includes the Arctic MAGs Catalogue and their functional annotation (European Bioinformatics Institute BioStudies ID: S-BSST451) and the co-assembly of metagenomic samples used to generate the metagenomic bins (European Nucleotide Archive PRJEB41575). Accession numbers for the metagenomic and metatranscriptomic samples used in the fragment recruitment analyses can be found in Supplementary Table 13. Publicly available datasets used in this study include the following: CheckM v.1.0.11 (https://github.com/Ecogenomics/CheckM/releases/tag/v1.1.0), GTDB release 89 (https://data.gtdb.ecogenomic.org/releases/release89/), SILVA 132 (https://www.arb-silva.de/documentation/release-132/), KEGG release 89.1 (https://www.genome.jp/kegg/docs/relnote.html) and Pfam database release 31.0 (http://ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam31.0/). Source data are provided with this paper Peer reviewed

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    Lirias
    Article . 2021
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    Nature Microbiology; OpenAIRE
    Other literature type . Article . 2021 . Peer-reviewed
    License: Springer Nature TDM
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    Authors: Régimbeau, Antoine; Budinich, Marko; Larhlimi, Abdelhalim; Pierella Karlusich, Juan José; +5 Authors

    AbstractStandard niche modelling is based on probabilistic inference from organismal occurrence data but does not benefit yet from genome‐scale descriptions of these organisms. This study overcomes this shortcoming by proposing a new conceptual niche that resumes the whole metabolic capabilities of an organism. The so‐called metabolic niche resumes well‐known traits such as nutrient needs and their dependencies for survival. Despite the computational challenge, its implementation allows the detection of traits and the formal comparison of niches of different organisms, emphasising that the presence–absence of functional genes is not enough to approximate the phenotype. Further statistical exploration of an organism's niche sheds light on genes essential for the metabolic niche and their role in understanding various biological experiments, such as transcriptomics, paving the way for incorporating better genome‐scale description in ecological studies.

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    Ecology Letters
    Other literature type . Article . 2022 . Peer-reviewed
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    Ecology Letters
    Article . 2021
    Hal-Diderot
    Preprint . 2021
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    Authors: Ina Maria Deutschmann; Anders K. Krabberød; Francisco Latorre; Erwan Delage; +7 Authors

    Background: Microbial interactions are fundamental for Earth’s ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. Omics-based censuses are helpful in predicting microbial interactions through the statistical inference of single (static) association networks. Yet, microbial interactions are dynamic and we have limited knowledge of how they change over time. Here, we investigate the dynamics of microbial associations in a 10-year marine time series in the Mediterranean Sea using an approach inferring a time-resolved (temporal) network from a single static network. Results: A single static network including microbial eukaryotes and bacteria was built using metabarcoding data derived from 120 monthly samples. For the decade, we aimed to identify persistent, seasonal, and temporary microbial associations by determining a temporal network that captures the interactome of each individual sample. We found that the temporal network appears to follow an annual cycle, collapsing, and reassembling when transiting between colder and warmer waters. We observed higher association repeatability in colder than in warmer months. Only 16 associations could be validated using observations reported in literature, underlining our knowledge gap in marine microbial ecological interactions. Conclusions: Our results indicate that marine microbial associations follow recurrent temporal dynamics in temperate zones, which need to be accounted for to better understand the functioning of the ocean microbiome. The constructed marine temporal network may serve as a resource for testing season-specific microbial interaction hypotheses. The applied approach can be transferred to microbiome studies in other ecosystems Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This project and IMD received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 675752 (ESR2, https://www.singek.eu) to RL. RL was supported by a Ramón y Cajal fellowship (RYC-2013–12554, MINECO, Spain). This work was also supported by the projects INTERACTOMICS (CTM2015-69936-P, MINECO, Spain), MicroEcoSystems (240904, RCN, Norway), and MINIME (PID2019-105775RB-I00, AEI, Spain) to RL. FL was supported by the Spanish National Program FPI 2016 (BES-2016–076317, MICINN, Spain). SC was supported by the CNRS MITI through the interdisciplinary program Modélisation du Vivant (GOBITMAP grant). DE and SC were supported by the H2020 project AtlantECO (award number 862923). A range of projects from the EU and the Spanish Ministry of Science funded the data collection and ancillary analyses at the BBMO. We acknowledge funding of the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000,928-S) 16 pages, 4 figures, 2 tables, supplementary information https://doi.org/10.1186/s40168-023-01523-z.-- Availability of data and materials: The BBMO microbial sequence abundances (ASV tables), taxonomic classifications, environmental data including nutrients, networks, and R-Markdowns for the data analysis including commands to run eLSA and EnDED (environmentally driven-edge-detection and computing Jaccard index) are publicly available: https://github.com/InaMariaDeutschmann/TemporalNetworkBBMO. DNA sequences are publicly available at the European Nucleotide Archive (https://www.ebi.ac.uk/ena; accession numbers PRJEB23788 for 18S rRNA genes & PRJEB38773 for 16S rRNA genes) Peer reviewed

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    https://www.biorxiv.org/conten...
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    Microbiome
    Article . 2022
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    Microbiome
    Article . 2023 . Peer-reviewed
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      Microbiome
      Article . 2022
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      Microbiome
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    Authors: Ina M. Deutschmann; Erwan Delage; Caterina R. Giner; Marta Sebastián; +9 Authors

    Microbial interactions are vital in maintaining ocean ecosystem function, yet their dynamic nature and complexity remain largely unexplored. Here, we use association networks to investigate possible ecological interactions in the marine microbiome among archaea, bacteria, and picoeukaryotes throughout different depths and geographical regions of the tropical and subtropical global ocean. Our findings reveal that potential microbial interactions change with depth and geographical scale, exhibiting highly heterogeneous distributions. A few potential interactions were global, meaning they occurred across regions at the same depth, while 11-36% were regional within specific depths. The bathypelagic zone had the lowest proportion of global associations, and regional associations increased with depth. Moreover, we observed that most surface water associations do not persist in deeper ocean layers despite microbial vertical dispersal. Our work contributes to a deeper understanding of the tropical and subtropical global ocean interactome, which is essential for addressing the challenges posed by global change Sampling was carried out thanks to the Consolider-Ingenio program (project Malaspina 2010 Expedition, ref. CSD2008–00077, to C.M.D.) and HOTMIX project (CTM2011-30010/MAR, to J.A.), funded by the Spanish Ministry of Economy and Competitiveness Science and Innovation. [...] I.M.D., R.L., and R.M. received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 675752 (SINGEK, http://www.singek.eu). R.L. was supported by a Ramón y Cajal fellowship (RYC-2013-12554, MINECO, Spain). This work was also supported by the projects INTERACTOMICS (CTM2015-69936-P, MINECO, Spain), MicroEcoSystems (240904, RCN, Norway), and MINIME (PID2019-105775RB-I00, AEI, Spain) to R.L. S.C. was supported by the CNRS MITI through the interdisciplinary program Modélisation du Vivant (GOBITMAP grant). S.C., D.E., and S.G.A. were funded by the H2020 project AtlantECO (award number 862923). We acknowledge funding of the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation to the ICM-CSIC (CEX2019-000928-S) 17 pages, 6 figures, 2 tables, supplementary information https://doi.org/10.1038/s41467-023-44550-y.-- Data availability: DNA sequence data is publicly available at the European Nucleotide Archive (see details in Table 2). The accession numbers for the different datasets are: MalaSurf (PRJEB23913, PRJEB25224), MalaVP (PRJEB23771, PRJEB45015), MalaDeep (PRJEB45011, PRJEB45014), Hotmix (PRJEB44683, PRJEB44474). OTU tables and source data to generate the figures and tables are provided in GitHub (https://github.com/InaMariaDeutschmann/GlobalNetworkMalaspinaHotmix) and Zenodo: https://doi.org/10.5281/zenodo.1023007337. The following databases have been used: SILVA v13289, PR2 v4.11.190, and the World Ocean Database 201391.-- Code availability: The code for data analysis, including commands to run FlashWeave and EnDED (environmentally-driven-edge-detection and computing Jaccard index), is publicly available at GitHub (https://github.com/InaMariaDeutschmann/GlobalNetworkMalaspinaHotmix) and Zenodo37 Peer reviewed

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    Nature Communications
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    Article . 2024 . Peer-reviewed
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      Nature Communications
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    Authors: Udita Chandola; Camille Trottier; Marinna Gaudin; Louis Josselin Lavier Aydat; +10 Authors

    AbstractNon-cyanobacteria diazotrophs (NCDs) were shown to dominate in surface waters shifting the long-held paradigm of cyanobacteria dominance and raising fundamental questions on how these putative heterotrophic bacteria thrive in sunlit oceans. Here, we report an unprecedented finding in the widely used model diatomPhaeodactylum tricornutum(Pt) of NCDs sustaining diatom cells in the absence of bioavailable nitrogen. We identifiedPtNCDs using metagenomics sequencing and detected nitrogenase genesin silicoand/or by PCR. We demonstrated nitrogen fixation withinPtNCDs while establishing their genetic affiliation to NCDs found in the environment. We showed the wide occurrence of this type of interactions with the isolation of NCDs from other microalgae, their identification in the environment, and their predicted associations with photosynthetic microalgae. Our findings will help to understand the different players driving global marine nitrogen fixation. Overall, this study provides evidence for a hitherto unrecognized symbiosis that affects marine element cycling and primary production using a multidisciplinary model-based approach.

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    https://doi.org/10.21203/rs.3....
    Preprint . 2023
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    Preprint . 2022
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    Authors: T. Uchida; J. Le Sommer; C. Stern; R. P. Abernathey; +26 Authors

    Abstract. With the increase in computational power, ocean models with kilometer-scale resolution have emerged over the last decade. These models have been used for quantifying the energetic exchanges between spatial scales, informing the design of eddy parametrizations, and preparing observing networks. The increase in resolution, however, has drastically increased the size of model outputs, making it difficult to transfer and analyze the data. It remains, nonetheless, of primary importance to assess more systematically the realism of these models. Here, we showcase a cloud-based analysis framework proposed by the Pangeo project that aims to tackle such distribution and analysis challenges. We analyze the output of eight submesoscale-permitting simulations, all on the cloud, for a crossover region of the upcoming Surface Water and Ocean Topography (SWOT) altimeter mission near the Gulf Stream separation. The cloud-based analysis framework (i) minimizes the cost of duplicating and storing ghost copies of data and (ii) allows for seamless sharing of analysis results amongst collaborators. We describe the framework and provide example analyses (e.g., sea-surface height variability, submesoscale vertical buoyancy fluxes, and comparison to predictions from the mixed-layer instability parametrization). Basin- to global-scale, submesoscale-permitting models are still at their early stage of development; their cost and carbon footprints are also rather large. It would, therefore, benefit the community to document the different model configurations for future best practices. We also argue that an emphasis on data analysis strategies would be crucial for improving the models themselves.

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    DOAJ
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    Authors: Tara Ocean Foundation; Abreu, Andre; Bourgois, Etienne; Gristwood, Adam; +45 Authors

    Microbial communities have essential roles in ocean ecology and planetary health. Microbes participate in nutrient cycles, remove huge quantities of carbon dioxide from the air and support ocean food webs. The taxonomic and functional diversity of the global ocean microbiome has been revealed by technological advances in sampling, DNA sequencing and bioinformatics. A better understanding of the ocean microbiome could underpin strategies to address environmental and societal challenges, including achievement of multiple Sustainable Development Goals way beyond SDG 14 ‘life below water’. We propose a set of priorities for understanding and protecting the ocean microbiome, which include delineating interactions between microbiota, sustainably applying resources from oceanic microorganisms and creating policy- and funder-friendly ocean education resources, and discuss how to achieve these ambitious goals We thank R. Zaayman-Gallant, T. Rauscher and F. Ibarbalz for preparation of the figures, and the European Union’s Horizon 2020 research and innovation project AtlantECO, under grant agreement no. 862923 With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S) This article is contribution number 131 of Tara Oceans.-- 11 pages, 5 figures, 1 table, 1 box Peer reviewed

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    Nature Microbiology
    Other literature type . Article . 2022 . Peer-reviewed
    License: Springer TDM
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    Authors: Churcheward, Benjamin; Millet, Maxime; Bihouée, Audrey; Fertin, Guillaume; +1 Authors

    ABSTRACTMetagenome-Assembled Genomes (MAGs) represent individual genomes recovered from metagenomic data. MAGs are extremely useful to analyse uncultured microbial genomic diversity, as well as to characterize associated functional and metabolic potential in natural environments. Recent computational developments have considerably improved MAGs reconstruction but also emphasized several limitations, such as the non-binning of sequence regions with repetitions or distinct nucleotidic composition. Different assembly and binning strategies are often used, however, it still remains unclear which assembly strategy in combination with which binning approach, offers the best performance for MAGs recovery. Several workflows have been proposed in order to reconstruct MAGs, but users are usually limited to single-metagenome assembly or need to manually define sets of metagenomes to co-assemble prior to genome binning. Here, we present MAGNETO, an automated workflow dedicated to MAGs reconstruction, which includes a fully-automated co-assembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAGs recovery. MAGNETO is implemented as a Snakemake workflow and is available at: https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto.IMPORTANCEGenome-resolved metagenomics has led to the discovery of previously untapped biodiversity within the microbial world. As the development of computational methods for the recovery of genomes from metagenomes continues, existing strategies need to be evaluated and compared to eventually lead to standardized computational workflows. In this study, we compared commonly used assembly and binning strategies and assessed their performance using both simulated and real metagenomic datasets. We propose a novel approach to automate co-assembly, avoiding the requirement for a priori knowledge to combine metagenomic information. The comparison against a previous co-assembly approach demonstrates a strong impact of this step on genome binning results, but also the benefits of informing co-assembly for improving the quality of recovered genomes. MAGNETO integrates complementary assembly-binning strategies to optimize genome reconstruction and provides a complete reads-to-genomes workflow for the growing microbiome research community.

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    mSystems
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      mSystems
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    Authors: Roy, Amédée; Fablet, Ronan; Bertrand, Sophie Lanco;

    Abstract Miniature electronic devices have recently enabled ecologists to document relatively large amounts of animal trajectories. Modelling such trajectories may contribute to explaining the mechanisms underlying observed behaviours and to clarifying ecological processes at the scale of the population by simulating multiple trajectories. Existing approaches to animal movement modelling have mainly addressed the first objective, and are often limited when used for simulation purposes. Individual‐based models generally rely on ad hoc formulation and their empirical parametrization lacks generability, while random walks based on mathematically sound statistical inference typically consist of first‐order Markovian models calibrated at the local scale which may lead to overly simplistic description and simulation of animal trajectories. We investigate a recent deep learning tool—generative adversarial networks (GAN)—to simulate animal trajectories. GANs consist of a pair of deep neural networks that aim to capture the data distribution of some experimental dataset. They enable the generation of new instances of data that share statistical properties. This study aims at identifying relevant deep network architectures to simulate central‐place foraging trajectories, as well as at evaluating GANs drawbacks and benefits over classical methods, such as state‐switching hidden Markov models (HMM). We demonstrate the outstanding ability of deep convolutional GANs to simulate and to capture medium‐ to large‐scale properties of seabird foraging trajectories. GAN‐derived synthetic trajectories reproduced the Fourier spectral density of observed trajectories better than those simulated using HMMs. However, unlike HMMs, GANs do not adequately capture local‐scale descriptive statistics, such as step speed distributions. GANs provide a new likelihood‐free approach to calibrate complex stochastic processes and thus open new research avenues for animal movement modelling. We discuss the potential uses of GANs in movement ecology and future developments to better capture local‐scale features. In this context, embedding HMM‐based priors in GAN schemes appears as a promising research direction.

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    Authors: Pauthenet, Etienne; Bachelot, Loïc; Balem, Kevin; Maze, Guillaume; +4 Authors

    Despite the ever-growing number of ocean data, the interior of the ocean remains undersampled in regions of high variability such as the Gulf Stream. In this context, neural networks have been shown to be effective for interpolating properties and understanding ocean processes. We introduce OSnet (Ocean Stratification network), a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The proposed scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T−S) profiles down to 1000 m and the mixed-layer depth (MLD) from surface data covering 1993 to 2019. OSnet is trained to fit sea surface temperature and sea level anomalies onto all historical in situ profiles in the Gulf Stream region. To achieve vertical coherence of the profiles, the MLD prediction is used to adjust a posteriori the vertical gradients of predicted T−S profiles, thus increasing the accuracy of the solution and removing vertical density inversions. The prediction is generalized on a 1/4∘ daily grid, producing four-dimensional fields of temperature and salinity, with their associated confidence interval issued from the bootstrap. OSnet profiles have root mean square error comparable with the observation-based Armor3D weekly product and the physics-based ocean reanalysis Glorys12. The lowest confidence in the prediction is located north of the Gulf Stream, between the shelf and the current, where the thermohaline variability is large. The OSnet reconstructed field is coherent even in the pre-Argo years, demonstrating the good generalization properties of the network. It reproduces the warming trend of surface temperature, the seasonal cycle of surface salinity and mesoscale structures of temperature, salinity and MLD. While OSnet delivers an accurate interpolation of the ocean stratification, it is also a tool to study how the ocean stratification relates to surface data. We can compute the relative importance of each input for each T−S prediction and analyse how the network learns which surface feature influences most which property and at which depth. Our results demonstrate the potential of machine learning methods to improve predictions of ocean interior properties from observations of the ocean surface.

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    Ocean Science (OS)
    Other literature type . 2022
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    https://doi.org/10.5194/egusph...
    Preprint . 2022 . Peer-reviewed
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    Authors: Marta Royo-Llonch; Pablo Sánchez; Clara Ruiz-González; Guillem Salazar; +36 Authors

    The role of the Arctic Ocean ecosystem in climate regulation may depend on the responses of marine microorganisms to environmental change. We applied genome-resolved metagenomics to 41 Arctic seawater samples, collected at various depths in different seasons during the Tara Oceans Polar Circle expedition, to evaluate the ecology, metabolic potential and activity of resident bacteria and archaea. We assembled 530 metagenome-assembled genomes (MAGs) to form the Arctic MAGs catalogue comprising 526 species. A total of 441 MAGs belonged to species that have not previously been reported and 299 genomes showed an exclusively polar distribution. Most Arctic MAGs have large genomes and the potential for fast generation times, both of which may enable adaptation to a copiotrophic lifestyle in nutrient-rich waters. We identified 38 habitat generalists and 111 specialists in the Arctic Ocean. We also found a general prevalence of 14 mixotrophs, while chemolithoautotrophs were mostly present in the mesopelagic layer during spring and autumn. We revealed 62 MAGs classified as key Arctic species, found only in the Arctic Ocean, showing the highest gene expression values and predicted to have habitat-specific traits. The Artic MAGs catalogue will inform our understanding of polar microorganisms that drive global biogeochemical cycles This work acknowledges the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). We thank the commitment of the following sponsors and research funding agencies: the Spanish Ministry of Economy and Competitiveness (project MAGGY, grant no. CTM2017-87736-R and Polar EcoGen PID2020-116489RB-I00), Horizon 2020-Research and Innovation Framework Programme (Atlantic ECOsystems assessment, forecasting & sustainability, grant no. H2020-BG-2019-2), Centre National de la Recherche Scientifique (in particular Groupement de Recherche GDR3280 and the Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans-GOSEE), European Molecular Biology Laboratory, Genoscope/Commissariat à l’Énergie Atomique et aux Énergies Alternatives, the French Ministry of Research and the French Government’s ‘Investissements d’Avenir’ programmes OCEANOMICS (project no. ANR-11-BTBR-0008), FRANCE GENOMIQUE (project no. ANR-10-INBS-09-08), MEMO LIFE (project no. ANR-10-LABX-54), Paris Sciences et Lettres University (project no. ANR-11-IDEX-0001-02), Eidgenössische Technische Hochschule Zürich and Helmut Horten Foundation, the Swiss National Foundation (project no. 205321_184955), MEXT/JSPS/KAKENHI (project nos. 16H06429, 16K21723, 16H06437 and 18H02279) 14 pages, 6 figures, additional information https://doi.org/10.1038/s41564-021-00979-9.-- Data availability: Accession numbers for the data used and generated in this study can be found in Supplementary Table 12, which includes the Arctic MAGs Catalogue and their functional annotation (European Bioinformatics Institute BioStudies ID: S-BSST451) and the co-assembly of metagenomic samples used to generate the metagenomic bins (European Nucleotide Archive PRJEB41575). Accession numbers for the metagenomic and metatranscriptomic samples used in the fragment recruitment analyses can be found in Supplementary Table 13. Publicly available datasets used in this study include the following: CheckM v.1.0.11 (https://github.com/Ecogenomics/CheckM/releases/tag/v1.1.0), GTDB release 89 (https://data.gtdb.ecogenomic.org/releases/release89/), SILVA 132 (https://www.arb-silva.de/documentation/release-132/), KEGG release 89.1 (https://www.genome.jp/kegg/docs/relnote.html) and Pfam database release 31.0 (http://ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam31.0/). Source data are provided with this paper Peer reviewed

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    Lirias
    Article . 2021
    Data sources: Lirias
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    Nature Microbiology; OpenAIRE
    Other literature type . Article . 2021 . Peer-reviewed
    License: Springer Nature TDM
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    Authors: Régimbeau, Antoine; Budinich, Marko; Larhlimi, Abdelhalim; Pierella Karlusich, Juan José; +5 Authors

    AbstractStandard niche modelling is based on probabilistic inference from organismal occurrence data but does not benefit yet from genome‐scale descriptions of these organisms. This study overcomes this shortcoming by proposing a new conceptual niche that resumes the whole metabolic capabilities of an organism. The so‐called metabolic niche resumes well‐known traits such as nutrient needs and their dependencies for survival. Despite the computational challenge, its implementation allows the detection of traits and the formal comparison of niches of different organisms, emphasising that the presence–absence of functional genes is not enough to approximate the phenotype. Further statistical exploration of an organism's niche sheds light on genes essential for the metabolic niche and their role in understanding various biological experiments, such as transcriptomics, paving the way for incorporating better genome‐scale description in ecological studies.

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    Ecology Letters
    Other literature type . Article . 2022 . Peer-reviewed
    License: CC BY NC ND
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    Ecology Letters
    Article . 2021
    Hal-Diderot
    Preprint . 2021
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    Authors: Ina Maria Deutschmann; Anders K. Krabberød; Francisco Latorre; Erwan Delage; +7 Authors

    Background: Microbial interactions are fundamental for Earth’s ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. Omics-based censuses are helpful in predicting microbial interactions through the statistical inference of single (static) association networks. Yet, microbial interactions are dynamic and we have limited knowledge of how they change over time. Here, we investigate the dynamics of microbial associations in a 10-year marine time series in the Mediterranean Sea using an approach inferring a time-resolved (temporal) network from a single static network. Results: A single static network including microbial eukaryotes and bacteria was built using metabarcoding data derived from 120 monthly samples. For the decade, we aimed to identify persistent, seasonal, and temporary microbial associations by determining a temporal network that captures the interactome of each individual sample. We found that the temporal network appears to follow an annual cycle, collapsing, and reassembling when transiting between colder and warmer waters. We observed higher association repeatability in colder than in warmer months. Only 16 associations could be validated using observations reported in literature, underlining our knowledge gap in marine microbial ecological interactions. Conclusions: Our results indicate that marine microbial associations follow recurrent temporal dynamics in temperate zones, which need to be accounted for to better understand the functioning of the ocean microbiome. The constructed marine temporal network may serve as a resource for testing season-specific microbial interaction hypotheses. The applied approach can be transferred to microbiome studies in other ecosystems Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This project and IMD received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 675752 (ESR2, https://www.singek.eu) to RL. RL was supported by a Ramón y Cajal fellowship (RYC-2013–12554, MINECO, Spain). This work was also supported by the projects INTERACTOMICS (CTM2015-69936-P, MINECO, Spain), MicroEcoSystems (240904, RCN, Norway), and MINIME (PID2019-105775RB-I00, AEI, Spain) to RL. FL was supported by the Spanish National Program FPI 2016 (BES-2016–076317, MICINN, Spain). SC was supported by the CNRS MITI through the interdisciplinary program Modélisation du Vivant (GOBITMAP grant). DE and SC were supported by the H2020 project AtlantECO (award number 862923). A range of projects from the EU and the Spanish Ministry of Science funded the data collection and ancillary analyses at the BBMO. We acknowledge funding of the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000,928-S) 16 pages, 4 figures, 2 tables, supplementary information https://doi.org/10.1186/s40168-023-01523-z.-- Availability of data and materials: The BBMO microbial sequence abundances (ASV tables), taxonomic classifications, environmental data including nutrients, networks, and R-Markdowns for the data analysis including commands to run eLSA and EnDED (environmentally driven-edge-detection and computing Jaccard index) are publicly available: https://github.com/InaMariaDeutschmann/TemporalNetworkBBMO. DNA sequences are publicly available at the European Nucleotide Archive (https://www.ebi.ac.uk/ena; accession numbers PRJEB23788 for 18S rRNA genes & PRJEB38773 for 16S rRNA genes) Peer reviewed

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    Microbiome
    Article . 2022
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    Microbiome
    Article . 2023 . Peer-reviewed
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    Article . 2023
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      Microbiome
      Article . 2022
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      Microbiome
      Article . 2023 . Peer-reviewed
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